Himanshu Padole*, S. D. Joshi, Tapan K. Gandhi
Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310
Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization method is used to design… More >
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